Titre: Stochastic representation and analysis of rough surface topography by random fields and integral geometry---Application to the UHMWPE cup involved in total hip arthroplasty.

Conférencier : Ola Ahmed, stagiaire postdoctoral au laboratoire LIV4D du département de génie informatique et génie logiciel de l'Ecole Polytechnique de Montréal

Résumé:

The statistical analysis and characterization of surface roughness provide interesting tools for predicting the performance of the components used in biomedical implants. Surface topography is, generally, composed of many length scales starting from its physical geometry, to its microscopic or atomic scales---known by roughness or micro-textures. Nowadays, the use of the advanced non-contact 3D optical imaging systems enables efficient characterization and analysis of surface roughness through geometric and statistical models. The focus of this presentation is to discuss new tools for micro-textures characterization by random fields and integral geometry frameworks.

First, an appropriate random field model of a rough surface is defined by the most significant statistical parameters that describe the distribution of surface irregularities. On the other hand, the decomposition of surface topography into multiple levels in terms of roughness parameters (e.g., height) allows for measuring its intrinsic volumes---known by Minkowski functionals. The combination of these frameworks provides a full description of surface micro-textures. More specifically, a skew-t random field model is proposed to model the shape asymmetries of the roughness. Based on this model, the intrinsic volumes of the surface have been calculated analytically and used to validate the model. Second, it is important to characterize the functionality of surfaces during interactions from the topography of micro-textures. A statistical analysis approach is therefore proposed to detect the variations of the shape asymmetries over time.

This method was validated on the application of total hip implant; in particular, the Ultra-High-Molecular-Weight polyethylene (UHMWPE) cup.
The results provided new insights for the aim of predicting the durability of the implants used in hip arthroplasty.

Bio:

Ola Ahmad earned the Ph.D. degree in Image-Vision-Signal from École Nationale Supérieure des Mines de Saint-Etienne (France) in 2013.
Between 2013 and 2015, she was a Postdoctoral Fellow in iCube-CNRS laboratory at the University of Strasbourg (France). She is currently a Postdoctoral Fellow in the Department of Computer and Software Engineering, Polytechnique Montréal. She is interested in random fields, spatiotemporal and scale-space statistical analysis with applications to image processing.

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